1. Technical Field
The present invention relates to water processing systems and more particularly, to an early detection of scaling processes in such systems, using statistical methods.
2. Discussion of the Related Art
There are many water treatment processes involving the flow of aqueous streams in opaque conduits wherein scaling of internal structures of said conduits will cause damage to the process or increase the resources needed to carry out such processes. Examples of such conduits with internal structures are heat exchangers containing heat transfer surfaces and pressure vessels containing membrane elements for water treatment.
The scaling referred to herein is the precipitation of sparingly soluble salts (including but not limited to sulfates of calcium, barium and strontium, calcium carbonate, silica and calcium fluoride) on the internal surfaces of these conduits due to the creation of supersaturation conditions on these internal surfaces by temperature or concentration gradients. By the time that the effects of such scaling are detected from process parameters such as from changes in temperature decreases or higher heating or cooling duties in a heat exchanger, or from product water flux-decline or increased applied pressure in membranes, irreversible damage could be caused to the equipment. At the least it could require costly downtime to repair the equipment. If there were an early warning system that would allow treating the equipment with preventative steps, it could be possible to keep the equipment operating without costly downtime or damage.
Because sparingly soluble salts usually have an induction time between the time that supersaturated conditions are obtained and the time that scaling of internal surfaces begins, one can take preventive actions just before, or at the end, of such an induction time by a variety of approaches. One example is the use of osmotic flushing to periodically sweep away supersaturated solutions and other foulants in reverse osmosis (RO) processes. Another example is the use of blowdown in cooling towers and flushing of heat exchangers with undersaturated solutions. Another solution is the use of flow reversal to replace supersaturated concentrate solution with undersaturated feed solution next to a desalination membrane. The problem is that if the early warning is too early and sensitive, then the preventative treatment sequence can be too frequent resulting in unnecessary use of chemicals, loss of production, or wear of equipment.
Because the equipment is inside an opaque conduit, it is not possible to visually inspect the equipment in real time during operation to determine when scaling occurs. One approach to overcoming the lack of optical transparency is to use an external device placed downstream of the equipment that is at risk to scaling and then optical units can be used to observe the onset of scaling. A similar known device monitors the flux decline in an external device downstream of the pressure housings containing membrane desalination modules. The disadvantage of such devices is that they are unable to exactly reproduce the conditions in the equipment in terms of flow patterns and supersaturation conditions, and can be either too sensitive (if they generate greater supersaturation than the actual equipment) or not sensitive enough (if they generate less local supesaturation than the actual equipment).
Another option is to use devices that do allow sampling of the conditions of the internal surfaces of an opaque conduit containing water processing equipment. A well known example is the use of ultrasound, which is used for detecting defects in welding in pipes and leaks inside conduits in the chemical-processing industry. A known method detects the presence of mineral deposits on water treatment membrane surfaces in both opaque flat sheet and commercial spiral wound pressure vessels. However, the methodology involved collecting the data and then analyzing the complex wave-forms generated offline due to the extensive analysis required. While this approach was effective as a diagnostic, it was not effective as an on-site early warning device that could be used for triggering process changes and preventive steps in real time. Part of the reason for this, is that enough time must pass for the signal to significantly deviate from the background signal and by direct inspection this often requires so much time that scaling has already proceeded to a much greater extent. Therefore this becomes a problem of statistically identifying a signal deviating from background noise.